public Color Sample(double u, double v)
{
//clears the network for each sample
network.ResetNetwork();
//computes the distance from the origin
double dist = (u * u) + (v * v);
dist = Math.Sqrt(dist);
//sets the input to the network
Vector input = new Vector(u, v, dist, 1.0);
network.SetInput(input);
//propagates until the maximum depth
for (int i = 0; i < DEPTH; i++)
{
network.Propergate();
}
//constructs the color from the output
Vector output = network.ReadOutput();
//double y = VMath.Sigmoid(output[0]);
//double a = Math.Tanh(output[1]) * 0.5;
//double b = Math.Tanh(output[2]) * 0.5;
double y = (output[0] + 1.0) * 0.5;
double a = output[1] * 0.5;
double b = output[2] * 0.5;
return(Color.FromYUV(y, a, b));
}
public Vector Evaluate(double a, double b, double ci)
{
//we must reset the network with each invocation
network.ResetNetwork();
//sets the input to the network
Vector input = new Vector(a, b, ci, 1.0);
network.SetInput(input);
//propagates until the maximum depth
for (int i = 0; i < DEPTH; i++)
{
network.Propergate();
}
//obtains the output from the network
return(network.ReadOutput());
}
public double Evaluate(double x, double y)
{
//we must reset the network with each invocation
network.ResetNetwork();
//sets the input to the network
Vector input = new Vector(x, y, 1.0);
network.SetInput(input);
//propagates until the maximum depth
for (int i = 0; i < DEPTH; i++)
{
network.Propergate();
}
//obtains the output from the network
Vector output = network.ReadOutput();
return(output[0]);
}
